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Aalborg Universitet
Smart Metering System for Microgrids
Palacios-Garcia, Emilio; Guan, Yajuan; Savaghebi, Mehdi; Quintero, Juan Carlos Vasquez;
Guerrero, Josep M.; Moreno-Munoz, Antonio; Ipsen, Brian S.
Published in:
IECON 2015, Yokohama, november 2015
DOI (link to publication from Publisher):
10.1109/IECON.2015.7392607
Publication date:
2015
Link to publication from Aalborg University
Citation for published version (APA):
Palacios-Garcia, E., Guan, Y., Savaghebi, M., Quintero, J. C. V., Guerrero, J. M., Moreno-Munoz, A., & Ipsen, B.
S. (2015). Smart Metering System for Microgrids. In IECON 2015, Yokohama, november 2015. (pp. 003289 003294). IEEE Press. DOI: 10.1109/IECON.2015.7392607
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www.microgrids.et.aau.dk
IECON2015-Yokohama
November 9-12, 2015
Smart Metering System for Microgrids
Emilio J. Palacios-Garcı́a1 , Student Member, IEEE, Yajuan Guan2 , Student Member, IEEE,
Mehdi Savaghebi2 , Member, IEEE, Juan C. Vásquez2 , Senior Member, IEEE, Josep M. Guerrero2 , Fellow, IEEE,
Antonio Moreno-Munoz1 , Senior Member, IEEE, and Brian S. Ipsen3
1 Department of Computer Architecture, Electronics and Electronic Technology. University of Cordoba, Spain.
2 Department of Energy Technology. Aalborg University, Denmark. 3 Kamstrup A/S, Denmark
[email protected], [email protected], [email protected], [email protected], [email protected], [email protected],
[email protected]
Abstract—Smart meters are the cornerstone in the new
conception of the electrical network or Smart Grid (SG),
providing detailed information about users’ energy consumption
and allowing the suppliers to remotely collect data for billing.
Nevertheless, their features are not only useful for the energy
suppliers, but they can also play a big role in the control of
the Microgrid since the recorded power and energy profiles
can be integrated in energy management systems (EMS). In
addition, basic power quality (PQ) disturbances can be detected
and reported by some advanced metering systems. Thus, this
paper will expose an example of Smart Meters integration in
a Microgrid scenario, which is the Intelligent Microgrid Lab
of Aalborg Univeristy (AAU). To do this, first the installation
available in the Microgrid Lab will be introduced. Then, three
different test scenarios and their respective results will be
presented, regarding the capabilities of this system and the
advantages of integrating the Smart Meters information in the
Microgrid control.
Index Terms—Advanced metering infrastructure, smart meter,
microgrid, energy management system, power quality
I. I NTRODUCTION
The concept of Smart Grid (SG) has completely changed the
conception of the electrical network, where the classical chain
of centralize energy production, transmission, and end users’
consumption has been substituted by a bi-directional power
flow, with distributed generation (DG) and consumption, and
new emerging types of energy resources. In this context
the Smart Meters play a main role, not only giving the
suppliers access to accurate data for billing, but also collecting
information of the end users, and establishing a two-ways
communication [1].
This new approach has been possible due to the
implementation of new communication protocols and a
complex metering infrastructure. In consequence, the direct
manual billing has been substituted by a metering network
composed of communication hubs, data concentrators, central
system units and different control centers, defining a new Grid
architecture [2].
In the beginning, these protocols were mainly proprietary
and the development of an advanced measuring network had
to face a wide range of issues. Nevertheless, a standardization
process has been promoted to assure the interconnection
This work was supported by the Technology Development and
Demonstration Program (EUDP) through the Sino-Danish Project Microgrid
Technology Research and Demonstration (meter.et.aau.dk).
between different Smart Meter units. In this context, protocols
such as DLMS/COSEM (IEC 62056-53 [3] and IEC 62056-62
[4]), SML (IEC 62056-58 [5]), M-Bus (EN 13757 [6])
or IEC 61850 [7], are today ones of the most common
implemented standard for Smart Meters and substations data
collection [8].
This standardization process has implied significant benefits
from the point of view of system integration and control
in Microgrid networks. A Microgrid can be described as an
electrical system where a bi-directional flow of energy exits. It
is usually composed of many DG units (renewable or not) and
the whole system can be either isolated or connected to the
main grid [9]. Therefore, to control this shared energy flow,
a well-developed communication infrastructure is an essential
part of the Microgrid conception [10].
Likewise, another important feature of the Microgrid
networks is the usage of a hierarchical control strategy. The
primary control is focused in the inner control of each DG unit
(droop control), the secondary control is supposed to restore
frequency and voltage amplitude deviations due to the inertias.
The tertiary control regulates the power flows between the
Microgrid [11], [12].
The reported frequency of the Smart Meters measures is
usually slow, 5 to 15 minutes, to be integrated into the
primary or secondary control. However, they can be employed
in a higher level for matching production and demand in
the Microgrid network. Thus, it is in the so-called tertiary
control level of the Microgrid where the Smart Meters can
provide measures for the EMS, allowing the implementation
of demand response (DR) techniques [13], [14].
In addition, some advanced Smart Meter systems report not
only information about energy consumption, but also basic
power quality (PQ) indices, with increasing importance due to
the proliferation of non-linear loads. These PQ measures can
range from voltage profile measurement to harmonic distortion
calculations [15], [16].
In this paper, the benefits of the Smart Meter systems for
the Microgrid will be addressed, showing a real example in a
lab-scale Microgrid. For this aim, the structure of this paper is
as following: in Section II the installation available in Aalborg
University Microgrid Lab for emulating a Microgrid network
is presented. Section III will provide different test scenarios
defined to evaluate the operation of the Smart Meters and
the obtained results will be commented. In Section IV, future
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2
works in the Microgrid Lab are described. Finally, Section V
is dedicated to the conclusions about the systems and tests.
II. S YSTEM OVERVIEW
The Smart Meter system is integrated into the Aalborg
University (AAU) Intelligent Microgrid Lab [17]. The whole
system is composed of 6 workstations that can be supplied
using a central DC source of up to 80 kW, PV panels,
Wind Turbines or flywheel storage. The general schema of
the Lab can be observed in Fig. 1. Each set-up includes
four DC-AC three phase converters with LCL output filters,
monitored switches for load operation and two Smart Meters
from Kamstrup Company.
EEPROM memory where the load profile information can be
stored. The amount of data that can be recorded depends on the
number of energy variables and integration period selected for
the energy. Thus, the number of days range from 450 days
when only the positive active energy is recorded every 60
minutes, down to 17 days, when positive and negative active
and reactive energy is monitored with 5 minutes of integration
period.
Another common feature of the two models is the
measurement of the voltage quality. The Smart Meter is able to
record deviations in the nominal voltage within a given range
and during a defined period of time. The default characteristic
of this function is minimum ±10% voltage deviation detection
for events longer than 10 minutes.
Finally, an additional characteristic only available in the
382L model is the possibility of defining a customized analysis
logger. This logger can register up to 16 different variables
with sampling periods of 5, 15, 45 or 60 minutes. Depending
on the number of variables and the selected resolution, the
logger’s depth can range from 2 days up to 520 days.
Therefore, in this model, not only the load profile is available,
but also the average voltage, current, power, etc. can be
recorded in different intervals [21].
B. Communication
Fig. 1. Overview of the Intelligent Microgrid Lab facilities
The four three phase converters can be individually
controlled using a dSPACE real-control platform managed
from a desktop PC. In addition, the different switches on the
system can be operated from this PC, enabling the remote
control of the whole system, that can be connected to a wide
range of load types.
A. Smart Meters
As mentioned before, each workstation contains two
Kamstrup Smart Meter, each one with a different purpose. The
industrial Smart Meter 351B is used to measure the energy
flow of the whole workstation, so it can measure DG, whilst
the other model the 382L is attached to the load consumption,
and therefore it can be regarded as an individual consumer.
Both models of Smart Meters are for three-phase
measurements. They measure active positive energy
(EN 50470-1 and EN 50470-3 [18]), reactive energy, and
active negative energy (IEC 62052-11 [19], IEC 62053-21
and IEC 62053-23 [20]). In addition, average and maximum
power values during the billing period and different tariffs
can be configured in the Smart Meter, recording individual
information in each tariff period.
Along with these basic features, as can be desirable in
any Smart Meter, additional characteristic are provided by
these models. The two employed Smart Meters have a built-in
Another important feature of the Smart Meters is the
implemented communication protocol. The Kamstrup meters
installed in the Microgrid Lab are compliance with the
IEC 62056 standard [3], which is the International Standard
version of the DLMS/COSEM specification (Device Language
Message Specification/Companion Specification for Energy
Metering). In particular, these Smart Meters support the
DLMS/COSEM specification over TCP/IP, GSM or over a
serial communication through an optical port.
From these three options, the communication procedure
implemented in the Lab is the TCP/IP connection since it
provides a scalable solution, where all the Smart Meters
installed in the Lab can be accessed using the TCP/IP
protocol over the same network structure. In addition, a
ZigBee Communication is implemented between the 382L
Smart Meter and an In-Home display only for global energy
monitoring. However, since the goal of the experiments is to
study the real detailed measures of the Smart Meters it has
not been used.
III. T EST S CENARIOS AND R ESULTS
In order to test the capabilities of the installed Smart
Meters, a simulated scenario was developed using one of the
workstations in the Microgrid Lab of Aalborg University. As
it can be observed in Fig. 2, the test set-up was composed of
one of the four three-phase DC-AC converters working as a
Voltage Source Inverter (VSI). This converter was supplying
two different loads (in both sides of the cabinet) connected in
parallel throw two individual monitored switches. In addition,
two system Smart Meters were monitoring the energy flow
between the converter and the two loads. Finally, all the
system was controlled from the desktop PC and the dSPACE,
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3
whereas the Smart Meters data were collected using a TCP/IP
communication and over an Ethernet connection.
In addition, these data can be exported in a wide range of
format. For the performed tests, the data were exported as
CSV files (Comma Separated Values) in order to build the
different graphs that will be subsequently shown.
A. Load Profile Measurement
The first scenario aims to simulate a power demand
variation, to test the capability of the Smart Meters for
measuring and recording the load profile of the system. For
this purpose, two different load were used, named as Load 1
and Load 2. Load 1 was an inductive load with 460 Ω and
50 mH per phase, whereas Load 2 was a pure resistive load
with 460 Ω per phase.
Using the two loads with different characteristic a simulated
power demand change was forced in the system. The VSI was
started together with Load 1 and after a defined period of time,
Load 2 was turned on. Finally, after 30 minutes, Load 2 was
switched off again.
The obtained results were recorded by the Smart Meter
using the load profile memory and were extracted in CSV
format using the MetorTool Kamstrup Software. The load
profile logger recorded four variables: active positive and
negative energy, and reactive positive and negative energy.
However, since the energy is only sunk by the loads the active
and reactive negative energy do not present any variation, so
they are not represented in Fig. 4. It should also denote that
the energy is an aggregated variable and therefore, the initial
value at the beginning of the experiment was not zero due
to the previous usage of the Smart Meter. Another important
consideration is that all the results will be represented as a
line-scatter plot where the real values of the Smart Meter
correspond to the points, whilst the line is just a linear
interpolation between adjacent measures.
Fig. 2. Workstation configuration for the tests
Fig. 3. Screenshot of Kamstrup MeterTool software.
Using this configuration, three different test scenarios were
analyzed. First, the possibilities of the Smart Meters to record
the power and energy demand profile were studied by means of
switching the two loads on and off. Secondly, the capability
of these Smart Meters to detect voltage quality events was
tested by changing the voltage reference of the three-phase
converter. Finally, the possibility to detect changes in the
network parameters due to the droop control strategy was
tested.
To obtain the data recorded by the Smart Meters, the
MeterTool software, provided by Kamstrup, was used. A
screenshot of the application is shown in Fig. 3. This
screenshot corresponds to the Analysis logger of the Smart
Meter where each column corresponds to a monitored variable.
Fig. 4. Scenario 1. Active and Reactive Energy during a Load Disturbance
As it can be seen in Fig. 4, the active energy (solid black
line with cross symbols) at the start of the test had a value of
99.7 kWh (left Y-Axis), whereas the reactive energy (solid
grey line with point symbols) equals to 2.24 kvarh (right
Y-Axis). Three different zones can be observed in the active
energy profile, characterized by a different slope. One from
13:30 h to 14:00 h, the second one from 14:00 h to 14:25 h and
the third one from 14:25 h until the end of the experiment. In
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4
contrast, the reactive energy profile seems to have an average
constant slope.
Fig. 5. Scenario 1. Active and Reactive Power during a Load Disturbance
These three zones clearly correspond with three different
power intensities in the active power consumption. This power
can be easily obtained from the energy profile since the
integration period is known (5 minutes). However, due to the
existence of an analysis logger in the 382L Smart Meter, the
system was configured to store the average active and reactive
power during each integration period. The different measures
obtained by the Smart Meter are graphically represented in
Fig. 5.
Now, the load variation can be observed in terms of power
consumption. As it was expected, after switch on the Load 2,
the active power measured by the Smart Meter rose up from
0.295 kW to 0.599 kW, whereas the reactive power kept a
constant value, since Load 2 was pure resistive. The opposite
happened between 14:25-14:30 h when Load 2 was turned off.
B. Voltage Quality Events Detection
The second test scenario was focused in the detection of
voltage quality events using the Smart Meter system. For this
aim, the DC-AC three phase converter was operated again as
a VSI, but changing now the voltage reference value for the
control loop. With this configuration two perturbations in the
voltage were generated, firstly an undervoltage and secondly
an overvoltage.
The detection of voltage quality events is recorded in a
logger inside both 351B and 382L Smart Meters. This logger
stores not only the deviations of the supplied voltage outside
the normal operation range, but it also detects and notice the
power cut off and the switch on events of the meter system.
In order to illustrate this behavior, a screenshot of the voltage
quality logger of one of the Smart Meters during the Test is
shown in Table. I.
The previously presented test and this second one were
carried out approximately from 13:30 h to 16:20 h. Before this
time, the Smart Meters were off and after the two scenarios
the system was again turned off. This can be perfectly seen in
the report of the voltage quality logger, where a power enabled
event was detected at 13:28 and a power cut off at 16:19.
Nevertheless, two additional events were also recorded in
between. One of the lines of the system seems to have
TABLE I
S MART M ETER VOLTAGE Q UALITY E VENTS L OGGER
a undervoltage during approximately 30 minutes. Thus, to
validate the occurrence of the event, before the start of the
simulation the 382L Smart Meter Analysis logger was also
configured to store the voltage in the 3 lines of the system.
Fig. 6 illustrates the evolution of the three phase voltages
during the test period where the voltage perturbation was
introduced. Again the real measured data are shown as
different points, whereas the line is just an interpolation. In
addition, two dashed lines have been represented to indicate
the maximum and minimum thresholds of the system, which
for these case were ±10% of the nominal voltage (230 V).
Fig. 6. Scenario 2. Voltage per phase during a variation in the reference
voltage of the VSI.
Fig. 6 shows that at the start of the test, the voltages of the
three phases of the system are within the admissible range.
Furthermore, a small unbalance can be observed between
three-phase, that can be justified since the real resistance and
inductance values of the load are not perfectly similar for all
the phases.
After approximately 10 minutes, the voltage of the system
was slowly decreased. This operation promoted that the
voltage in line 3 reached a value outside the nominal range.
The first sampled value outside range was reported to be
203 V at 13:15 h. However, regarding Fig. I and Fig. 6it
can be noticed that the event occurs before the refresh of
the register. This depicts an important fact in the behavior
of the system. In spite of updating the measuring registers
after the integration period (5 minutes), the instantaneous
values of voltage and current are measured to calculate power
and energy, otherwise it would have been impossible to have
reported the undervoltage event with a precision of seconds.
The undervoltage situation is maintained during
approximately 30 minutes. After this time, the voltage
is restored to the nominal value. As it was illustrated in
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5
Fig. I, the system is also able to detect and record the instant
when the voltage is again between the limits.
Subsequently, an overvoltage situation is forced. However,
in this case, the voltage was selected so that it was still within
the limits to prove the behavior of the system tolerance for
voltage quality event detection. As it can be observed in Fig.
6, the voltage in line 1 reached a value near to the maximum
threshold. The value of this voltage is equal 252 V, a figure
which is inside the limits since 230+10% is 253 V. Thus, as
it was expected no voltage quality event is reported in Fig. I
during this period.
(a) Voltage and Reactive Power Profiles
C. Monitoring of Droop Control
The third and final scenario was focused in the detection
of deviations in the system parameters, due to the control
operations of the Microgrid, specifically the droop control.
This control strategy is employed to regulate the active
and reactive energy power flow by means of changing the
frequency and voltage of the DGs respectively, a behavior that
can be clearly observed in the Microgrid when it is working
in islanded mode, which means without connection to the
main grid, and no voltage and frequency restoration strategy
(secondary control) are implemented.
The operation mode of the droop control is summarized by
the equations (1) and (2), where ω is the angular frequency
of the DG, E is the voltage, and P and Q are the active
and reactive power exchanged with the Microgrid. In the
other hand, k p and kv are the droop coefficients, which
determines the slope of the control strategy and, therefore, the
frequency and voltage deviation for achieving a determined
power exchange.
Δω = −k p · P
(1)
ΔE = −kv · Q
(2)
Since the Smart Meters installed in the Lab are not able to
measure frequency, only the droop control strategy for reactive
power exchange was tested. For this aim, the same loads of
the Scenario 1 were used, but activating now the droop control
in the DC-AC converter, which works as a VSI emulating a
DG unit. In addition, the switch on sequence was inverted, so
now the Load 2 (pure resistive load) is first turned on, and
subsequently Load 1 (inductive load) is activated to cause a
change in the demanded reactive power.
The obtained results are shown in Fig. 7, where the
three-phase voltages are represented together with the
demanded reactive power. As it can be seen, from 12:50 h
to 13:05 h, only Load 2 is activate, so no reactive power
consumption is observed, and therefore the voltage of all the
lines is around the reference, which was set to be 220 V.
However, Load 1 is activated between 13:05-13:10 h, so
the DG needs to supply now reactive energy. Since the droop
control is activated, the system starts to decrease the nominal
voltage in order to fulfill the requirements of the load.
The voltage deviation can be observed in Fig. 7(b), where a
screenshot of the analysis logger of the MeterTool software is
shown. The average deviation in all the phases was observed
(b) MeterTool Analysis Logger Screenshot
Fig. 7. Scenario 3. Voltage and Reactive Power during a load disturbance,
using droop control.
to be 2 V, whereas the average change in the reactive power
demand was 50 var. Thus, the theoretical droop coefficient kv ,
that was selected to be equal to -0.05, can be compared with
the measured value. This is illustrated in (3).
−2
ΔE
=
= −0.04
(3)
ΔQ
50
As it can be seen this value is very near to the theoretical
one, and the observed error might be due to the resolution
of the measures of the Smart Meter. Therefore, the Smart
Meters have demonstrated to be able to detect not only load
disturbance and voltage quality issues, but they can also notice
the influence of the different control strategies employed in
the Microgrid context, when neither frequency nor voltage
restoration strategy is implemented, and the Microgrid is
working in islanded mode.
kv =
IV. F UTURE W ORK
The presented metering infrastructure inside the Microgrid
Lab will be soon substituted by a more advanced and powerful
system which will be based on the OMNIA Suite of Kamstrup.
OMNIA Suite is an advanced metering infrastructure (AMI)
which includes Smart Meter units, data concentrators, network
communication and data management and storage, so the Lab
will be able to emulate a SG [21].
As it is shown in Fig. 8, all the Smart Meters within
the system will be connected using the Wireless standard
(EN 13757-5 [6]) using the radio mesh communication
version. In addition, others Smart Meter systems regarding
district heating and water will be implemented, using Wireless
M-Bus Standard (EN 13757-4 [6]), to have a complete
simulation of a real system.
www.microgrids.et.aau.dk
6
to implements not only power and energy monitoring but also
to detect some basic PQ issues.
Furthermore, although the measures from the Smart Meters
cannot be directly used for the inner control loops, the system
has shown to be able to detect voltage deviations due to
the operations strategies of the Microgrid, such as the Droop
control for the regulation of reactive power flow.
Regarding these ideas, the future implementation that will
be carried out in the Microgrid Lab has been exposed.
This new Smart Metering system will provide a centralized
communication and data collection, more advanced PQ
measurement and the integration with other systems.
R EFERENCES
Fig. 8. Conceptual schema of the future Smart Metering Infrastructure in the
Microgrid Lab of Aalborg University.
The data from the Smart Meter will be collected in
a data concentrator using the radio mesh communication.
As well, this data concentrator will be connected to a
PC using Ethernet, GPRS or 3G, and the DLMS/COSEM
R
will
standard. Inside the PC a software called UtiliDriver
be responsible for integrating the data from the Smart Meter
with the rest of the system.
This central software will not store any data, but it will
allow the intercommunication with the Smart Meters using
an Open Web Service API (Application Protocol Interface).
Thus, the protocol complexity is simplified, so the Smart Meter
measurements can be integrated into others systems with a
relatively low complexity.
V. C ONCLUSIONS
The work has presented the benefits of integrating Smart
Metering systems inside the electrical grid and specifically in
Microgrids. A real application of the Smart Meters to monitor
a workstation in the Aalborg University Microgrid Lab was
commented, showing three test scenarios for studying the
load profile measurement, the voltage quality events detection,
and the droop control strategy, and having discussing their
respective results.
The Smart Meters have demonstrated their capability to
report in the most basic case at least the energy consumption
profile and in other more advanced systems the power, voltage
and current, too. All of this is due to the wide range of
communication possibilities offered by the Smart Meter, which
have allowed us to monitor the system from a central PC.
In addition, the report frequency for these energy and power
measurements has been observed to range between 5 minutes
and 1 hour integration interval, a period that can be fast enough
for implementing EMS and DR strategies in a tertiary control
level without additional equipment and investment.
The system has also shown the possibility of detect voltage
quality events with a resolution of 1 second. This depicts that
the real sampling frequency of the system to measure current
and voltage is much faster. Therefore, the Smart Metering
system can be seen as the most suitable part of the system
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